Algorithms for American XVA and free boundary calculations with stochastic counterparty default intensity
Bibliographic record
Abstract
Credit and total valuation adjustments (CVA and XVA) are significant in equity markets, as parts of the risk management under Basel III framework. In addition, path-dependent derivatives, such as American-type ones, are heavily traded in markets. Therefore, it is important to accurately and efficiently compute valuation adjustments for American-type derivatives. In this paper, we derive a two-dimensional (2D) in space partial differential equation (PDE) for pricing American-type derivatives including the XVA, assuming the counterparty default risk follows a mean reversion stochastic process, while the self-party has constant default risk. We reformulate the time-dependent, 2D nonlinear PDE into penalty form, which includes two nonlinear source terms. We employ the double-penalty iteration for the 2D PDE to resolve the two nonlinear terms, while we use a finite difference scheme for the spatial discretization, and Crank–Nicolson-Rannacher timestepping. We introduce algorithms for the accurate calculation of the free boundary. We also formulate an asymptotic approximation technique, similar to the one developed for the European case problem, but adjusted for the American put option problem. A key step is to derive the asymptotic approximation to the free boundary for the American put option. We present numerical experiments in order to study the accuracy and effectiveness of the 2D PDE and asymptotic approximations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".